物流市場數位孿生、機會、成長動力、產業趨勢分析與預測，2024-2032

2023 年，物流市場的全球數位孿生估值為 12 億美元，預計 2024 年至 2032 年複合年成長率將超過 25.7%。到路線最佳化，透過即時洞察顯著提高營運效率。

最終用戶擴大將數位孿生與人工智慧 (AI) 和機器學習 (ML) 技術整合在一起。這種融合增強了數位孿生的預測能力，從而實現更敏銳的預測和最佳化。人工智慧和機器學習演算法篩選來自數位孿生的大量資料，辨別模式並做出即時決策。例如，在路線最佳化中，人工智慧增強的數位孿生可以考慮交通、天氣和歷史資料，即時修改送貨路線。

物流業的數位孿生分為組件、部署模型、應用程式、最終用戶和區域。

市場依組件分為軟體和服務。 2023 年，軟體領域的銷售額約為 8.93 億美元。物聯網 (IoT) 設備和感測器的整合顯著增強了數位孿生軟體的功能。這些增強功能有助於從物流網路內的資產、車輛和基礎設施收集即時資料。這些詳細的資料對於製作有形系統的精確數位複製品至關重要。例如，2024 年 3 月，DHL 利用數位孿生技術製作其倉庫的虛擬模型。

市場根據部署模式將物流中的數位孿生分為基於雲端的和本地的。預計到 2032 年，基於雲端的細分市場將超過 75 億美元。在高峰時期或不可預見的高峰期間，企業可以迅速升級其基礎設施，而無需大量資本支出。這種適應性不僅可以確保最佳性能，還可以提高效率和客戶滿意度。

2023年，北美在物流市場的數位孿生中處於領先地位，佔據約31%的收入佔有率。在美國的引領下，該地區處於技術進步的前沿。物聯網、人工智慧和巨量資料分析的快速發展和採用對於推動數位孿生在物流領域的應用至關重要。該地區的公司利用這些技術來提高營運效率、完善決策並確保競爭優勢。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

• 產業生態系統分析
• 供應商格局
  • 軟體供應商
  • 物流服務商
  • 技術提供者
  • 終端用戶
• 利潤率分析
• 技術和創新格局
• 專利分析
• 重要新聞和舉措
• 監管環境
• 衝擊力
  • 成長動力
    • 對物流營運即時洞察的需求不斷成長
    • 對數據驅動決策的需求不斷成長
    • 物流業的技術進步
    • 物流企業越來越注重降低成本
  • 產業陷阱與挑戰
    • 數據整合挑戰
    • 數位孿生實施複雜性
• 成長潛力分析
• 波特的分析
• PESTEL分析

第 4 章：競爭格局

• 介紹
• 公司市佔率分析
• 競爭定位矩陣
• 戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

• 主要趨勢
• 軟體
• 服務
  • 託管服務
  • 專業服務
    • 諮詢服務
    • 整合和實施服務
    • 支援和維護服務

第 6 章：市場估計與預測：按部署模型，2021 - 2032 年

• 主要趨勢
• 基於雲端
• 本地

第 7 章：市場估計與預測：按應用分類，2021 - 2032

• 主要趨勢
• 路線最佳化
• 倉庫和庫存管理
• 預測性維護
• 資產追蹤
• 其他

第 8 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

• 主要趨勢
• 汽車
• 航太和國防
• 製造業
• 零售及電子商務
• 能源和公用事業
• 其他

第 9 章：市場估計與預測：按地區，2021 - 2032

• 主要趨勢
• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 英國
  • 德國
  • 法國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 北歐人
  • 歐洲其他地區
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳新銀行
  • 東南亞
  • 亞太地區其他地區
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 拉丁美洲其他地區
• MEA
  • 南非
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • MEA 的其餘部分

第 10 章：公司簡介

• AAG IT Services
• AVEVA (Schneider Electric Group)
• Blue Yonder
• Bosch Rexroth
• Dassault Systemes
• General Electric
• IBM
• Kinaxis, Inc.
• Microsoft Solutions
• Oracle
• SAP
• Siemens Digital Industries Software
• Simio LLC
• The Anylogic Company

The Global Digital Twin in Logistics Market was valued at USD 1.2 billion in 2023 and is projected to grow at a CAGR of over 25.7% from 2024 to 2032. By creating a virtual replica of their physical logistics network, companies can monitor and analyze every facet of their operations, from warehouse management to route optimization, significantly boosting operational efficiency through real-time insights.

End-users are increasingly integrating digital twins with artificial intelligence (AI) and machine learning (ML) technologies. This fusion amplifies the predictive prowess of digital twins, leading to sharper forecasting and optimization. AI and ML algorithms sift through vast data from digital twins, discerning patterns and making instantaneous decisions. For example, in route optimization, AI-enhanced digital twins can modify delivery routes in real-time, factoring in traffic, weather, and historical data.

The digital twin in logistics industry is bifurcated into component, deployment model, application, end user, and region.

The market is segmented by component into software and services. In 2023, the software segment accounted for roughly USD 893 million. The capabilities of digital twin software have been significantly bolstered by the integration of Internet of Things (IoT) devices and sensors. These enhancements facilitate real-time data gathering from assets, vehicles, and infrastructure within the logistics network. Such detailed data is vital for crafting precise digital replicas of tangible systems. For instance, in March 2024, DHL harnessed digital twin technology to craft virtual models of its warehouses.

The market categorizes the digital twin in logistics by deployment model into cloud-based and on-premises. The cloud-based segment is projected to surpass USD 7.5 billion by 2032. These cloud solutions offer unparalleled scalability, allowing logistics firms to modulate computing resources in response to demand shifts. During peak times or unforeseen surges, businesses can swiftly upscale their infrastructure without hefty capital outlays. This adaptability not only ensures peak performance but also bolsters efficiency and customer satisfaction.

In 2023, North America led the digital twin in logistics market, capturing about 31% of the revenue share. Spearheaded by the U.S., this region stands at the vanguard of technological advancements. The swift evolution and adoption of IoT, AI, and big data analytics are pivotal in driving the uptake of digital twins in logistics. Companies in this region harness these technologies to boost operational efficiency, refine decision-making, and secure a competitive edge.

Table of Contents

Chapter 1 Methodology and Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates and calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimation
• 1.3 Forecast model
• 1.4 Primary research and validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Software providers
  • 3.2.2 Logistics service providers
  • 3.2.3 Technology providers
  • 3.2.4 End-user
• 3.3 Profit margin analysis
• 3.4 Technology and innovation landscape
• 3.5 Patent analysis
• 3.6 Key news and initiatives
• 3.7 Regulatory landscape
• 3.8 Impact forces
  • 3.8.1 Growth drivers
    • 3.8.1.1 Growing demand for real-time insights into logistics operations
    • 3.8.1.2 Rising need for data-driven decision-making
    • 3.8.1.3 Technological advancements in the logistics industry
    • 3.8.1.4 Growing focus of logistics companies on cost reduction
  • 3.8.2 Industry pitfalls and challenges
    • 3.8.2.1 Data integration challenges
    • 3.8.2.2 Digital twin implementation complexity
• 3.9 Growth potential analysis
• 3.10 Porter's analysis
• 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2032 ($Bn)

• 5.1 Key trends
• 5.2 Software
• 5.3 Services
  • 5.3.1 Managed services
  • 5.3.2 Professional services
    • 5.3.2.1 Consulting services
    • 5.3.2.2 Integration and implementation services
    • 5.3.2.3 Support and maintenance services

Chapter 6 Market Estimates and Forecast, By Deployment Model, 2021 - 2032 ($Bn)

• 6.1 Key trends
• 6.2 Cloud-based
• 6.3 On-premises

Chapter 7 Market Estimates and Forecast, By Application, 2021 - 2032 ($Bn)

• 7.1 Key trends
• 7.2 Route optimization
• 7.3 Warehouse and inventory management
• 7.4 Predictive maintenance
• 7.5 Asset tracking
• 7.6 Others

Chapter 8 Market Estimates and Forecast, By End User, 2021 - 2032 ($Bn)

• 8.1 Key trends
• 8.2 Automotive
• 8.3 Aerospace and defense
• 8.4 Manufacturing
• 8.5 Retail and E-commerce
• 8.6 Energy and utilities
• 8.7 Others

Chapter 9 Market Estimates and Forecast, By Region, 2021 - 2032 ($Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Nordics
  • 9.3.8 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 South Korea
  • 9.4.5 ANZ
  • 9.4.6 Southeast Asia
  • 9.4.7 Rest of Asia Pacific
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
  • 9.5.4 Rest of Latin America
• 9.6 MEA
  • 9.6.1 South Africa
  • 9.6.2 Saudi Arabia
  • 9.6.3 UAE
  • 9.6.4 Rest of MEA

Chapter 10 Company Profiles

• 10.1 AAG IT Services
• 10.2 AVEVA (Schneider Electric Group)
• 10.3 Blue Yonder
• 10.4 Bosch Rexroth
• 10.5 Dassault Systemes
• 10.6 General Electric
• 10.7 IBM
• 10.8 Kinaxis, Inc.
• 10.9 Microsoft Solutions
• 10.10 Oracle
• 10.11 SAP
• 10.12 Siemens Digital Industries Software
• 10.13 Simio LLC
• 10.14 The Anylogic Company